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首页> 外文会议>International Conference on String Processing and Information Retrieval(SPIRE 2005); 20051102-04; Buenos Aires(AR) >Comparison of Representations of Multiple Evidence Using a Functional Framework for IR
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Comparison of Representations of Multiple Evidence Using a Functional Framework for IR

机译：使用IR的功能框架比较多个证据的表示形式

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摘要

The combination of sources of evidence is an important subject of research in information retrieval and can be a good strategy for improving the quality of rankings. Another active research topic is modeling and is one of the central tasks in the development of information retrieval systems. In this paper, we analyze the combination of multiple evidence using a functional framework, presenting two case studies of the use of the framework to combine multiple evidence in contexts bayesian belief networks and in the vector space model. This framework is a meta-theory that represents IR models in a unique common language, allowing the representation, formulation and comparison of these models without the need to carry out experiments. We show that the combination of multiple evidence in the bayesian belief network can be carried at in of several ways, being that each form corresponds to a similarity function in the vector model. The analysis of this correspondence is made through the functional framework. We show that the framework allows us to design new models and helps designers to modify these models to extend them with new evidence sources.
机译：证据来源的组合是信息检索研究的重要课题，并且可以是提高排名质量的良好策略。另一个活跃的研究主题是建模，它是信息检索系统开发中的中心任务之一。在本文中，我们使用功能框架分析了多个证据的组合，并提供了两个使用该框架在上下文贝叶斯信念网络和向量空间模型中组合多个证据的案例研究。该框架是一种以独特的通用语言表示IR模型的元理论，无需进行实验即可表示，表示和比较这些模型。我们表明，贝叶斯信念网络中多个证据的组合可以通过几种方式进行，因为每种形式都对应于矢量模型中的相似性函数。通过功能框架对此对应关系进行分析。我们证明了该框架使我们能够设计新模型，并帮助设计人员修改这些模型以使用新的证据来源进行扩展。

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